Mission: Iconic Reefs and National Coral Reef Monitoring Program Benthic Analysis

Authors:
Nicole Krampitz¹, Julia Mateski¹, Dione Swanson^2,3
¹ CSS, Inc. under contract to NOAA National Ocean Service, National Centers for Coastal Ocean Science
² NOAA National Marine Fisheries Service, Southeast Fisheries Science Center
³ University of Miami, Cooperative Institute for Marine and Atmospheric Studies

1 Introduction

Mission: Iconic Reefs (M:IR) is a multi-institutional initiative designed to restore ecological function and biodiversity across key reef sites in the Florida Keys. Led by NOAA in collaboration with federal, state, institutional, and non-profit partners, M:IR targets nearly three million square feet of reef at seven key coral reefs in the Florida Keys National Marine Sanctuary (FKNMS): Carysfort (North and South), Cheeca Rocks, Eastern Dry Rocks, Horseshoe Reef, Looe Key, Newfound Harbor, and Sombrero Reef (Figure 1). Across these reefs, M:IR aims to restore coral cover to an average of 25% across the seven sitesal– an inititaitve unparalleled in scope and scale that will require hundreds of thousands of coral restoration outplants and over a decade of phased intervention.

These Iconic reefs were selected for restoration due to their diversity in reef types, levels of anthropogenic disturbances, initial coral cover, geographic distribution, and cultural significance. Restoration progress at these reefs is informed by ongoing monitoring of coral reef benthic communities and coral populations at M:IR sites using a range of metrics, such as coral cover, size structure, mortality prevalence, and diversity. Such multi-metric monitoring is essential to evaluating whether current restoration practices are effective in establishing and maintaining a healthy coral reef, as restoration ‘success’ encompasses a range of ecological and management metrics rather than a single measurement (e.g., coral cover) (Goergen et al., 2020).

In addition to monitoring within the M:IR reefs, it is also important to assess the surrounding reefs in the Florida Keys to interpret restoration progress. The National Coral Reef Monitoring Program (NCRMP), which conducts standardized reef assessments across the Florida Keys on a biennial basis, provides a solid framework for comparison. To align with this program, the M:IR team applied NCRMP survey methodology within its Iconic Sites in 2022 and 2024 to complement NCRMP surveys. These surveys focused on shallow (<30 m), hard-bottom reef habitats, using a stratified-random, one-stage design within 50 m × 50 m grid cells to ensure representative sampling across depth and rugosity strata (Ault et al., 2021). The survey design ensures that survey sites are allocated by cross-shelf zone, rugosity type, and depth strata, with sites distributed around the Florida Keys from nearshore to offshore to a maximum depth of 30 m. The NCRMP sample grid was spatially joined with the seven distinct M:IR zones (Figure 1). Survey sites were proportionally allocated within NCRMP strata to maintain consistent representation across habitats.

Corals and benthic communities were monitored using two different NCRMP surveys: the Benthic Community Assessment survey and the Coral Demographics survey (NOAA CRCP 2022a, 2022b, 2024a, 2024b). The Benthic Community Assessment survey includes: 1) benthic cover (%) estimates along a 15-m transect with a line point intercept method, 2) presence/absence of Endangered Species Act (ESA)-listed coral species (NOAA National Marine Fisheries Service, 2014), 3) abundance of key macroinvertebrates, and 4) reef rugosity measurements within a 15 m x 2 m belt-transect area (NOAA CRCP 2022a, 2024b). At the same site, coral demographics were surveyed within a 10 m x 1 m belt-transect area (NOAA CRCP 2022b, 2024b). NCRMP coral demographic survey data were combined with complementarily allocated survey data from Florida Fish and Wildlife Conservation Commission’s Disturbance Response Monitoring (DRM). In all coral demographics surveys, all live coral colonies ≧ 4 cm were counted, identified to species, measured to the nearest centimeter, and estimates were made of the proportion per colony of any present mortality (recent or old), disease (absent, present: slow, fast), and/or bleaching (none, total, partial, paling). Only live coral colonies were included in the survey; dead colonies with 100% mortality were not surveyed (e.g., colonies killed by coral disease). Juvenile corals (< 4 cm) were reported for species richness only and were not included in counts, size measurements, or estimates of condition (NOAA CRCP 2022b); in 2024, juveniles of select coral species were counted (NOAA CRCP 2024b). In 2024, NCRMP benthic surveys also included large area imaging (LAI) collected at most survey sites: analyses of these LAI data are in progress and not presented here.

To allow direct comparisons of benthic communities inside the restoration areas with the Florida Keys reefs, the NCRMP dataset was restricted to strata types overlapping M:IR sites and depths less than 12 meters. A majority of the reef habitats within the seven M:IR areas ranged from 0-12 meters depth. Statistical comparisons were conducted between M:IR and NCRMP estimates for each survey year and between years for M:IR and NCRMP. This report focuses on coral populations and benthic community metrics; a separate report can be found for fish population assessments (Blondeau et al., 2025).

2 Benthic Cover Sites

In 2022, a total of 584 surveys occurred (Table 1) across 403 sites (Florida Tech Memo 2022). In 2024, 771 surveys occured across 542 sites (Grove et al., in prep)

2.1 Benthic Composition

Table 1. Number of demographic (Demo) and benthic community assessment (BCA) surveys completed by M:IR, NCRMP, and DRM in 2022 and 2024.

	M:IR		NCRMP		DRM
	Demo	BCA	Demo	BCA	Demo
2022	90	89	95	92	218
2024	100	100	129	129	313
Total	190	189	224	221	531

Table 2. Number of Benthic Commnity Assesement (BCA) sites sampled inside and outside M:IR areas in each stratum.
Study Area	Strata Name	Strat Description	2022 Sample Number	2024 Sample Number
Inside (M:IR)	CFK01	Inshore reef, all relief types and depths	8	10
Inside (M:IR)	CFK02	Mid-channel patch reef, all relief types and depths	12	12
Inside (M:IR)	CFK03	Offshore patch reef, all relief types and depths	7	8
Inside (M:IR)	CFK04	Forereef, low relief, shallow (0-6 m)	--	3
Inside (M:IR)	CFK05	Forereef, high relief, shallow (0-6 m)	24	33
Inside (M:IR)	CFK06	Forereef, low relief, mid-shallow (6-12 m)	4	9
Inside (M:IR)	CFK07	Forereef, high relief, mid-shallow (6-12 m)	32	28
Outside (NCRMP)	CFK01	Inshore reef, all relief types and depths	10	9
Outside (NCRMP)	CFK02	Mid-channel patch reef, all relief types and depths	21	13
Outside (NCRMP)	CFK03	Offshore patch reef, all relief types and depths	9	8
Outside (NCRMP)	CFK04	Forereef, low relief, shallow (0-6 m)	--	6
Outside (NCRMP)	CFK05	Forereef, high relief, shallow (0-6 m)	7	9
Outside (NCRMP)	CFK06	Forereef, low relief, mid-shallow (6-12 m)	9	7
Outside (NCRMP)	CFK07	Forereef, high relief, mid-shallow (6-12 m)	10	28

2.2 Coral Demography

Table 3. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).
Study Area	Strata Name	Strat Description	2022 Sample Number	2024 Sample Number
Inside (M:IR)	CFK01	Inshore reef, all relief types and depths	12	10
Inside (M:IR)	CFK02	Mid-channel patch reef, all relief types and depths	16	12
Inside (M:IR)	CFK03	Offshore patch reef, all relief types and depths	7	11
Inside (M:IR)	CFK04	Forereef, low relief, shallow (0-6 m)	--	4
Inside (M:IR)	CFK05	Forereef, high relief, shallow (0-6 m)	25	38
Inside (M:IR)	CFK06	Forereef, low relief, mid-shallow (6-12 m)	4	12
Inside (M:IR)	CFK07	Forereef, high relief, mid-shallow (6-12 m)	34	34
Outside (NCRMP + DRM)	CFK01	Inshore reef, all relief types and depths	27	24
Outside (NCRMP + DRM)	CFK02	Mid-channel patch reef, all relief types and depths	79	109
Outside (NCRMP + DRM)	CFK03	Offshore patch reef, all relief types and depths	37	42
Outside (NCRMP + DRM)	CFK04	Forereef, low relief, shallow (0-6 m)	4	31
Outside (NCRMP + DRM)	CFK05	Forereef, high relief, shallow (0-6 m)	21	45
Outside (NCRMP + DRM)	CFK06	Forereef, low relief, mid-shallow (6-12 m)	19	33
Outside (NCRMP + DRM)	CFK07	Forereef, high relief, mid-shallow (6-12 m)	60	61

Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, *Millepora*, Seagrass, bare substrate, and other miscellaneous organisms.

2.3 Focal Species

Table 4. Most Observed Species and Focal M:IR Species Each Year
Species	Colonies in 2022	Colonies in 2024
Siderastrea siderea	3,868	5,618
Porites astreoides	2,657	2,132
Stephanocoenia intersepta	1,345	1,534
Agaricia agaricites	884	237
Porites porites	776	558
*Montastraea cavernosa	437	645
Orbicella faveolata	266	237
Orbicella annularis	211	156
Siderastrea radians	182	328
Porites furcata	160	77
Porites divaricata	118	100
*Dichocoenia stokesii	106	137
*Colpophyllia natans	103	76
Solenastrea bournoni	79	113
*Acropora cervicornis	74	13
*Diploria labyrinthiformis	50	65
*Pseudodiploria strigosa	50	46
Eusmilia fastigiata	45	75
*Pseudodiploria clivosa	23	31
*Acropora palmata	14	NA
*Meandrina meandrites	5	11
* indicates M:IR focal species

3 Point of Contact:

NCRMP Atlantic Benthic Team Lead: Shay Viehman (shay.viehman@noaa.gov)

4 References

Ault, J. S., Smith, S. G., Luo, J., Grove, L. J., Johnson, M. W., & Blondeau, J. (2021). Refinement of the Southern Florida Reef Tract Benthic Habitat Map with Habitat Use Patterns of Reef Fish Species [Dataset]. NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/archive/accession/0224176

Goergen, E. A., Schopmeyer, S., Moulding, A. L., & Moura, A. (2020). Coral Reef Restoration Monitoring Guide: Methods to evaluate restoration success from local to ecosystem scales (Technical Report NOS NCCOS 279). National Oceanic; Atmospheric Administration. https://doi.org/10.25923/xndz-h538

Grove, L. J. W., Viehman, T. S., Blondeau, J., Cain, E., Edwards, K. F., Krampitz, N., Langwiser, C., Mateski, J., Sturm, A. B., Swanson, D., & Towle, E. K. (in prep). National Coral Reef Monitoring Program, Biological Monitoring Summary – Florida: 2024 [Technical Memorandum NOS CRCP, in preparation]. NOAA.

NOAA Coral Reef Conservation Program (CRCP). (2024a). National Coral Reef Monitoring Program (NCRMP) Benthic Community Assessment Survey Field Protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, and U.S. Virgin Islands (p. 29). NOAA Coral Reef Conservation Program. https://doi.org/10.25923/e3gf-vk84

NOAA Coral Reef Conservation Program (CRCP). (2024b). National Coral Reef Monitoring Program (NCRMP) Coral Demographics Survey Field Protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, and U.S. Virgin Islands (p. 28). NOAA Coral Reef Conservation Program. https://doi.org/10.25923/hgpr-jm80

NOAA Coral Reef Conservation Program (NOAA CRCP). (2022a). National Coral Reef Monitoring Program (NCRMP) Benthic Community Assessment Survey Field Protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, and U.S. Virgin Islands–2022. NOAA Coral Reef Conservation Program. https://doi.org/10.25923/0708-8333

NOAA Coral Reef Conservation Program [NOAA]. (2022b). National Coral Reef Monitoring Program (NCRMP) Coral Demographics Survey Field Protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, and U.S. Virgin Islands–2022. NOAA Coral Reef Conservation Program. https://doi.org/10.25923/9a1r-m911

Data Sources:
National Coral Reef Monitoring Program Florida Benthic (NCRMP)
Florida Fish and Wildlife Conservation Commission’s Disturbance Response Monitoring (DRM)

--- title: "Mission: Iconic Reefs and National Coral Reef Monitoring Program Benthic Analysis" subtitle: "Summary Report for 2022 and 2024" bibliography: References.bib reference-location: section reference-section-title: Bibliography reference-section-spacing: 20em nocite: "@NOAA2022a; @NOAA2022b; @NOAA2024a; @NOAA2024b" csl: special.csl link-citations: true format: html: theme: lumen toc: true number-sections: true code-fold: true code-tools: true fig-cap-location: bottom fig-align: center self-contained: true title-block-banner: true title-block-style: default include-before-body: logos.html include-in-header: text: | <style> .csl-entry { margin-bottom: .7em; } </style> pdf: fig-cap-location: bottom author: "Nicole Krampitz, Julia Mateski, and Dione Swanson" toc: true toc-title: | \clearpage Table of Contents fig-numbering: false number-sections: true table-caption: false include-in-header: text: | \usepackage{graphicx} \usepackage{titling} \pretitle{% \begin{center} \includegraphics[width=6cm]{mir_logo.png}\\[1em] \includegraphics[width=4cm]{noaa_crcp.jpg}\\[1em] \LARGE } \usepackage{caption} \captionsetup[figure]{labelformat=empty} \captionsetup[table]{labelformat=empty} execute: freeze: auto echo: true warning: false message: false --- \newpage ## Introduction [Mission: Iconic Reefs](https://www.fisheries.noaa.gov/southeast/habitat-conservation/restoring-seven-iconic-reefs-mission-recover-coral-reefs-florida-keys) (M:IR) is a multi-institutional initiative designed to restore ecological function and biodiversity across key reef sites in the Florida Keys. Led by NOAA in collaboration with federal, state, institutional, and non-profit partners, M:IR targets nearly three million square feet of reef at seven key coral reefs in the Florida Keys National Marine Sanctuary (FKNMS): Carysfort (North and South), Cheeca Rocks, Eastern Dry Rocks, Horseshoe Reef, Looe Key, Newfound Harbor, and Sombrero Reef (Figure 1). Across these reefs, M:IR aims to restore coral cover to an average of 25% across the seven sitesal-- an inititaitve unparalleled in scope and scale that will require hundreds of thousands of coral restoration outplants and over a decade of phased intervention. ![Figure 1: Map of the Florida Keys showing the sites of Mission: Iconic Reefs](../Project/images/MIR%20Map.png){fig-align="center"} These Iconic reefs were selected for restoration due to their diversity in reef types, levels of anthropogenic disturbances, initial coral cover, geographic distribution, and cultural significance. Restoration progress at these reefs is informed by ongoing monitoring of coral reef benthic communities and coral populations at M:IR sites using a range of metrics, such as coral cover, size structure, mortality prevalence, and diversity. Such multi-metric monitoring is essential to evaluating whether current restoration practices are effective in establishing and maintaining a healthy coral reef, as restoration 'success' encompasses a range of ecological and management metrics rather than a single measurement (e.g., coral cover) [@Goergen2020]. In addition to monitoring within the M:IR reefs, it is also important to assess the surrounding reefs in the Florida Keys to interpret restoration progress. The National Coral Reef Monitoring Program (NCRMP), which conducts standardized reef assessments across the Florida Keys on a biennial basis, provides a solid framework for comparison. To align with this program, the M:IR team applied NCRMP survey methodology within its Iconic Sites in 2022 and 2024 to complement NCRMP surveys. These surveys focused on shallow (\<30 m), hard-bottom reef habitats, using a stratified-random, one-stage design within 50 m × 50 m grid cells to ensure representative sampling across depth and rugosity strata [@Ault2021]. The survey design ensures that survey sites are allocated by cross-shelf zone, rugosity type, and depth strata, with sites distributed around the Florida Keys from nearshore to offshore to a maximum depth of 30 m. The NCRMP sample grid was spatially joined with the seven distinct M:IR zones (Figure 1). Survey sites were proportionally allocated within NCRMP strata to maintain consistent representation across habitats. \ ```{r libraries, include = FALSE} # LIBRARIES library(ggplot2) library(dplyr) library(tidyr) library(cowplot) library(grid) library(patchwork) library(ggthemes) library(extrafont) library(grid) library(ggthemes) library(ggpattern) library(extrafont) library(plotly) library(tidyverse) library(ncrmp.benthics.analysis) library(ggplot2) library(cowplot) library(gridExtra) library(egg) library(mgcv) # source("R Scripts/total_dis_code.R") source("../Project/R Scripts/MIR_make_size_bins.R") source("../Project/R Scripts/theme_publication.R") source("../Project/R Scripts/QuadPanel.R") # source("R Scripts/bleaching_estimates.R") # source("R Scripts/total_bleach_code.R") source("../Project/R Scripts/dis_ble_prev_ratio_est.R") ``` ```{r MIR species, echo = FALSE} MIR_species <- c( "ACR CERV", "ACR PALM", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS_ AST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE" ) ``` Corals and benthic communities were monitored using two different NCRMP surveys: the Benthic Community Assessment survey and the Coral Demographics survey (NOAA CRCP 2022a, 2022b, 2024a, 2024b). The Benthic Community Assessment survey includes: 1) benthic cover (%) estimates along a 15-m transect with a line point intercept method, 2) presence/absence of Endangered Species Act (ESA)-listed coral species (NOAA National Marine Fisheries Service, 2014), 3) abundance of key macroinvertebrates, and 4) reef rugosity measurements within a 15 m x 2 m belt-transect area (NOAA CRCP 2022a, 2024b). At the same site, coral demographics were surveyed within a 10 m x 1 m belt-transect area (NOAA CRCP 2022b, 2024b). NCRMP coral demographic survey data were combined with complementarily allocated survey data from Florida Fish and Wildlife Conservation Commission's Disturbance Response Monitoring ([DRM](https://coraldrm.org/)). In all coral demographics surveys, all live coral colonies ≧ 4 cm were counted, identified to species, measured to the nearest centimeter, and estimates were made of the proportion per colony of any present mortality (recent or old), disease (absent, present: slow, fast), and/or bleaching (none, total, partial, paling). Only live coral colonies were included in the survey; dead colonies with 100% mortality were not surveyed (e.g., colonies killed by coral disease). Juvenile corals (\< 4 cm) were reported for species richness only and were not included in counts, size measurements, or estimates of condition (NOAA CRCP 2022b); in 2024, juveniles of select coral species were counted (NOAA CRCP 2024b). In 2024, NCRMP benthic surveys also included large area imaging (LAI) collected at most survey sites: analyses of these LAI data are in progress and not presented here. To allow direct comparisons of benthic communities inside the restoration areas with the Florida Keys reefs, the NCRMP dataset was restricted to strata types overlapping M:IR sites and depths less than 12 meters. A majority of the reef habitats within the seven M:IR areas ranged from 0-12 meters depth. Statistical comparisons were conducted between M:IR and NCRMP estimates for each survey year and between years for M:IR and NCRMP. This report focuses on coral populations and benthic community metrics; a separate report can be found for fish population assessments (Blondeau et al., 2025). ## Benthic Cover Sites In 2022, a total of 584 surveys occurred (Table 1) across 403 sites (Florida Tech Memo 2022). In 2024, 771 surveys occured across 542 sites [@GroveInPrep] ### Benthic Composition ```{r Benthic Survey Summaries, results='asis', echo=FALSE} if (knitr::is_html_output()) { TableCaption <- "<div style='font-size:0.9em; color: #666666; margin-bottom:0.5em; text-align: center;'> Table 1. Number of demographic (Demo) and benthic community assessment (BCA) surveys completed by M:IR, NCRMP, and DRM in 2022 and 2024. </div>" cat(TableCaption) #pulling the whole table from a different script because it's messy due to the format source("../Project/R Scripts/html_summary_table.R") } else if (knitr::is_latex_output()) { #for pdf, using a photo of the table from excel instead because format is very messy cat("\\begin{center}\n") #changing the table caption to be more similar to html, with light coloring and cnetering cat("\\textcolor{darkgray}{\\textbf{Table 1.} Number of demographic (Demo) and benthic community assessment (BCA) surveys completed by M:IR, NCRMP, and DRM in 2022 and 2024.}\n\n") #this is the photo of the table cat("\\includegraphics[width=\\linewidth]{../Project/images/Proper_Table.png}\n") #this centers the photo cat("\\end{center}\n") } ``` ```{r prepare data for benthic cover table, include = FALSE} ##Let's just start with MIR/NCRMP only (benthic cover) NCRMP_MIR <- read_csv("../Project/data/NCRMP_MIR_cora_2022_24_psu_grps.csv") %>% group_by(YEAR, strat_cora, analysis_group) %>% summarise(count = n()) %>% filter(analysis_group != "NCRMP_NA") %>% rename(STRAT = strat_cora, PROT = analysis_group) %>% mutate(description = case_when( STRAT == "CFK01" ~ "Inshore reef, all relief types and depths", STRAT == "CFK02" ~ "Mid-channel patch reef, all relief types and depths", STRAT == "CFK03" ~ "Offshore patch reef, all relief types and depths", STRAT == "CFK04" ~ "Forereef, low relief, shallow (0-6 m)", STRAT == "CFK05" ~ "Forereef, high relief, shallow (0-6 m)", STRAT == "CFK06" ~ "Forereef, low relief, mid-shallow (6-12 m)", STRAT == "CFK07" ~ "Forereef, high relief, mid-shallow (6-12 m)")) table <- NCRMP_MIR %>% pivot_wider(id_cols = STRAT:description, names_from = YEAR, values_from = count) %>% mutate(`Study area` = case_when( PROT == "MIR_GRP"~ "Inside (M:IR)", PROT == "NCRMP_GRP" ~ "Outside (NCRMP)" )) %>% select(`Study area`, STRAT, description, `2022`, `2024`) %>% arrange(`Study area`, STRAT) # #conditional statement for rendering in both HTML and PDF # if (knitr::is_html_output()) { # DT::datatable(table, # class = "cell-border stripe", # rownames = FALSE, # colnames = c("Study Area", "Strata Name", "Strata Description", "2022 Sample Number", "2024 Sample Number"), # caption = "Table 1. Number of Benthic Commnity Assesement (BCA) sites sampled inside and outside M:IR areas in each stratum.", # options = list( # columnDefs = list(list(className = 'dt-center', targets = "_all")), # info = FALSE, # paging = FALSE, # searching = FALSE # ) # )} name <- c("Study Area", "Strata Name", "Strat Description", "2022 Sample Number", "2024 Sample Number") # # invisible(table) ``` ```{r create benthic cover table, echo = FALSE, results = 'asis'} table <- table %>% ungroup() %>% mutate(across(everything(), ~ ifelse(is.na(.), "--", .))) if (knitr::is_html_output()) { ####### HTML VERSION ############## sjPlot::tab_df( table, title = "<div style='text-align:center'>Table 2. Number of Benthic Commnity Assesement (BCA) sites sampled inside and outside M:IR areas in each stratum.</div>", escape = FALSE, show.footnote = TRUE, alternate.rows = TRUE, col.header = name, CSS = list( css.caption = "font-weight: normal;", css.tdata = c(rep("", ncol(table) - 2), "text-align: center;", "text-align: center;") ) ) ######### PDF VERSION ########### } else if (knitr::is_latex_output()) { # Manually print a styled caption with label BEFORE the table cat("\\textcolor{darkgray}{\\textbf{Table 2.} Number of Benthic Community Assessment (BCA) sites sampled inside and outside M:IR areas in each stratum.}\n") # Print the table itself in kableExtra kableExtra::kbl( table, booktabs = TRUE, caption = NULL, # no caption here align = c("l", "l", "l", "c", "c"), col.names = c("Study Area", "Strat", "Strat Description", "2022", "2024") ) %>% kableExtra::kable_styling( latex_options = c("striped", "hold_position"), font_size = 10, position = "center" ) %>% kableExtra::row_spec(0, bold = TRUE, align = "c") %>% print() } ``` ### Coral Demography ```{r create coral demo table data, include = FALSE} ###Demographics (including DRM) DRM <- read_csv("../Project/data/DRM_cora_2022_24_psu_grps.csv") %>% group_by(YEAR, strat_cora, analysis_group) %>% summarise(count = n()) %>% filter(analysis_group != "NCRMP_NA") %>% rename(STRAT = strat_cora, PROT = analysis_group) %>% mutate(description = case_when( STRAT == "CFK01" ~ "Inshore reef, all relief types and depths", STRAT == "CFK02" ~ "Mid-channel patch reef, all relief types and depths", STRAT == "CFK03" ~ "Offshore patch reef, all relief types and depths", STRAT == "CFK04" ~ "Forereef, low relief, shallow (0-6 m)", STRAT == "CFK05" ~ "Forereef, high relief, shallow (0-6 m)", STRAT == "CFK06" ~ "Forereef, low relief, mid-shallow (6-12 m)", STRAT == "CFK07" ~ "Forereef, high relief, mid-shallow (6-12 m)")) %>% rbind(., NCRMP_MIR) %>% group_by(STRAT, PROT, YEAR, description) %>% summarise(count = sum(count)) %>% ungroup() table_2 <- DRM %>% pivot_wider(id_cols = STRAT:description, names_from = YEAR, values_from = count) %>% mutate(`Study area` = case_when( PROT == "MIR_GRP"~ "Inside (M:IR)", PROT == "NCRMP_GRP" ~ "Outside <br> (NCRMP + DRM)" )) %>% select(`Study area`, STRAT, description, `2022`, `2024`) %>% arrange(`Study area`, STRAT) # # if (knitr::is_html_output()) { # DT::datatable(table_2, # class = "cell-border stripe", # rownames = FALSE, # colnames = c("Study Area", "Strata Name", "Strata Description", "2022 Sample Number", "2024 Sample Number"), # caption = "Table 3. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).", # options = list( # columnDefs = list(list(className = 'dt-center', targets = "_all")), # info = FALSE, # paging = FALSE, # searching = FALSE # ) # )} # # invisible(table2) ``` ```{r print coral demo table, results = 'asis', echo = FALSE} table_2 <- table_2 %>% ungroup() %>% mutate(across(everything(), ~ ifelse(is.na(.), "--", .))) ####### HTML VERSION ############## if (knitr::is_html_output()) { sjPlot::tab_df( table_2, title = "<div style='text-align:center'> Table 3. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).</div>", escape = FALSE, show.footnote = TRUE, alternate.rows = TRUE, col.header = name, CSS = list( css.firsttablecol = "width: 140px;", # widen first column css.caption = "font-weight: normal;", css.tdata = c(rep("", ncol(table) - 2), "text-align: center;", "text-align: center;")) ) ######### PDF VERSION ########### } else if (knitr::is_latex_output()) { table_2 <- table_2 %>% mutate(`Study area` = case_when( `Study area` == "Outside <br> (NCRMP + DRM)" ~ "Outside (NCRMP + DRM)", TRUE ~ `Study area`##PDF doesn't accept break )) # Styled caption printed manually before the table cat("\\textcolor{darkgray}{\\textbf{Table 3.} The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).}\n") # Print the table with kableExtra kableExtra::kbl( table_2, booktabs = TRUE, caption = NULL, col.names = c("Study Area", "Strata", "Strata Description", "2022", "2024") ) %>% kableExtra::kable_styling( latex_options = c("striped", "hold_position"), font_size = 10, position = "center" ) %>% kableExtra::row_spec(0, bold = TRUE, align = "c") %>% print() } ``` ```{r fig 1: benthic cover data, fig.width=12, fig.asp=0.6, echo = FALSE} #get cover categories cat_codes <- read_csv("../Project/data/bcov_cat_codes.csv") %>% dplyr::rename(cover_group = CODE) #load and clean cover data (MIR and NCRMP, no DRM) cvr <- read_csv("../Project/data/fk2022_24_NCRMP_MIR_bcov_catcd_dom_NK.csv")%>% dplyr::rename( cover_group = COVER_CAT_CD, REGION = analysis_group, avCvr = wavp, SE = se_wp, n_sites = n ) %>% left_join(cat_codes) ``` ```{r fig_benthic_cover_data (OG), echo = FALSE} #wanted_cover_cats <- c("HCORA" , "MACAL") #Formatting Data for Combined Macroalgal/Hard Coral plot cvr_analysis <- cvr %>% group_by(REGION, COV_CAT_A, YEAR) %>% # filter(COV_CAT_A %in% wanted_cover_cats) %>% summarise( avCvr = sum(avCvr), SE = sqrt(sum(SE^2))#, # LCI_1.96 = avCvr - 1.96 * SE, # UCI_1.96 = avCvr + 1.96 * SE, # LCI = avCvr - SE, # UCI = avCvr + SE ) #change names --> more readable new_dat <- cvr_analysis %>% mutate(COV_CAT_A = recode(COV_CAT_A, "HCORA" = "Hard Coral", "MACAL" = "Macroalgae", "RAMIC" = "Ramicrusta", "TURFA" = "Turf Algae", "SPONG" = "Sponge", "SCORA" = "Octocoral", "CCALG" = "Crustose Coralline Algae (CCA)", "OTHER" = "Other" )) ##Other includes Cyanobacteria, Bare, Palythoa, Other organisms, Millepora, Seagrass ## read in significance sig <- read_csv("../Project/data/significance.csv") dodge_width <- 0.06 # Get unique categories categories <- unique(new_dat$COV_CAT_A) # Define the plot for multi-function use in tabset make_plot <- function(df, category) { df_filtered <- df %>% filter(COV_CAT_A == category) plot <- df_filtered %>% ggplot(aes(x = as.integer(YEAR), y = avCvr *100, group = interaction(COV_CAT_A, REGION), color = REGION)) + geom_point(size = 2, position = position_dodge(width = 0.06)) + geom_line(size = 1, position = position_dodge(width = 0.06)) + geom_errorbar(aes(ymin = pmax(0, avCvr * 100 - SE * 100), ymax = avCvr * 100 + SE * 100), width = 0.1, position = position_dodge(width = 0.06)) + scale_color_manual( values = c("MIR_GRP" = "#0072B2", "NCRMP_GRP" = "#D55E00"), labels = c("M:IR", "NCRMP"), name = "" ) + labs(x = "Year", y = "Cover (%)", title = category ) + # facet_wrap(~ COV_CAT_A) + ##This was when we filtered for just hard corals/macroalgae. If we output as a pdf, might be necessary to bring back scale_x_continuous(breaks = c(2022, 2024)) + coord_cartesian(ylim = c(-1, NA)) + #scale_y_continuous(, labels = scales::percent_format(accuracy = 2) ) + theme_Publication(base_size = 20) + theme( strip.text = element_text(face = "bold", size = 16), legend.position = "bottom", legend.direction = "horizontal", legend.key.size = unit(1.2, "cm"), legend.margin = margin(0, 0, 0, 0, "cm"), legend.text = element_text(size = 16), legend.background = element_blank(), legend.title = element_blank() ) + guides(color = guide_legend(override.aes = list(size = 4))) ##conditional formatting for ramicrusta so it doesn't come up as negative if (category == "Ramicrusta"){ plot <- plot + coord_cartesian(ylim = c(-0, 4)) } ##Adding the significance labels #This may take a fair amount of tweaking ## sig_cover <- sig %>% filter(metric == "cover") %>% filter(variable == category) # For between years find a midpoint to go on the plot midpoints <- df_filtered %>% group_by(REGION) %>% summarise( y_mid = 1.02 * mean(avCvr * 100, na.rm = TRUE)) # For between groups find the y min to go on the plot ymin <- df_filtered %>% group_by(COV_CAT_A) %>% summarise(y_min = 85 * (min(avCvr)- max(SE))) %>%# this hopefully gets a nice plot minimum just below wherever it ends dplyr::rename(variable = COV_CAT_A) #for joining purposes # Add them to the significance table sig_cover <- sig_cover %>% left_join(midpoints, by = "REGION") %>% left_join(ymin, by = "variable") ##Plot it with annotations (plot + ### Significance BETWEEN GROUPS on X axis geom_text(data = sig_cover %>% filter (is.na(REGION)), aes(x = YEAR, y = -0.5), label = "+", inherit.aes = FALSE, size = 12, color = "black") + ### Significance BETWEEN YEARS on graph geom_text(data = sig_cover %>% filter (is.na(YEAR)), aes(x = 2023, y = y_mid, color = REGION), label = "*", inherit.aes = FALSE, size = 15)) } ``` ```{r COVER HTML LOOP, fig.cap="Figure 2. Cover estimates for main benthic groups inside and outside M:IR areas for 2022 and 2024. A black (+) indicates a significant difference between groups in that survey year (i.e., 2022 or 2024). A colored (*) indicates significance difference between years for the given group (i.e., M:IR or NCRMP). The category ‘Other’ comprises Cyanobacteria, Palythoa, <i>Millepora</i>, Seagrass, bare substrate, and other miscellaneous organisms.", fig.width=9, results='asis', fig.asp=0.8, echo=FALSE} # Define manual tab order ordered_categories <- c("Hard Coral", "Macroalgae", "Octocoral", "Sponge", "Turf Algae", "Crustose Coralline Algae (CCA)", "Ramicrusta", "Other") if (knitr::is_html_output()){ # Loop through categories and print plots cat("::: {.panel-tabset}\n\n") # open tabset container walk(ordered_categories, function(cat) { cat("### ", cat, " {.tab-pane}\n\n", sep = "") cat_plot <- make_plot(new_dat, cat) print(cat_plot) cat("\n\n") }) cat(":::\n") # close tabset container } ``` ### Focal Species ```{r make data for top species, include = FALSE} ##Everything calculated for density is done so by region/year, so maybe just do total colonies observed? df <- read.csv( "../Project/data/FK2024_NCRMP_DRM_MIR_corsz_AR_NK.csv") %>% filter(N > 0, analysis_group != "NCRMP_NA") %>% group_by(SPECIES_CD, YEAR) %>% summarise(observed = n()) %>% arrange(desc(observed)) %>% pivot_wider(id_cols = SPECIES_CD, names_from = YEAR, values_from = observed) ##Top 15 species top <- df %>% ungroup() %>% dplyr::slice(1:15) ##MIR Species target_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS_ AST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE") MIR <- df %>% filter( SPECIES_CD %in% target_species ) ##Together table <- full_join(MIR, top) ##Add names from origial DRM data names <- read_csv("../Project/data/DRM_FLK_2014_2024_2stage_coral_demographics.csv") %>% select(SPECIES_CD, SPECIES_NAME) %>% unique() final <- left_join(table, names) %>% select(SPECIES_NAME, SPECIES_CD, `2022`, `2024`) %>% arrange(desc(`2022`)) %>% mutate(SPECIES_NAME = ifelse( `SPECIES_CD` %in% target_species, paste0("*", "<i>", SPECIES_NAME, "</i>"), paste0("<i>", SPECIES_NAME, "</i>") )) %>% mutate(across(c(`2022`, `2024`), ~ format(., big.mark = ",", scientific = FALSE))) %>% ungroup() %>% select(-SPECIES_CD) ``` ```{r create top species table, echo = FALSE, results = 'asis'} final <- final %>% ungroup() %>% mutate(across(everything(), ~ ifelse(is.na(.), "--", .))) ######### HTML OUTPUT ############### if (knitr::is_html_output()) { sjPlot::tab_df( final, title = "<div style='text-align:center'>Table 4. Most Observed Species and Focal M:IR Species Each Year</div>", alternate.rows = TRUE, show.footnote = TRUE, footnote = "* indicates M:IR focal species", col.header = c( "<b>Species</b>", "<b>Colonies in 2022</b>", "<b>Colonies in 2024</b>" ), CSS = list( css.caption = "font-weight: normal;", css.table = "margin-left: auto; margin-right: auto; width: 90%;", css.tdata = c(rep("", ncol(table) - 2), "text-align: center;", "text-align: center;") )) ######### PDF OUTPUT ############### } else if (knitr::is_latex_output()) { ##remove the italics styling for HTML final <- final %>% mutate(SPECIES_NAME = gsub("</?i>", "", SPECIES_NAME)) # Custom caption printed manually above the table cat("\\textcolor{darkgray}{\\textbf{Table 4.} Number of colonies observed for focal M:IR species and the most commonly encountered non-focal species in 2022 and 2024.}\\\\\n") # Render table with kableExtra kableExtra::kbl( final, booktabs = TRUE, col.names = c("Species", "Colonies in 2022", "Colonies in 2024"), align = c("l", "c", "c") ) %>% kableExtra::kable_styling( latex_options = c("striped", "hold_position"), font_size = 10, position = "center" ) %>% kableExtra::row_spec(0, bold = TRUE, align = "c") %>% kableExtra::column_spec(1, italic = TRUE) %>% print() } ``` ## Point of Contact: **NCRMP Atlantic Benthic Team Lead:** Shay Viehman ([shay.viehman\@noaa.gov](mailto:shay.viehman@noaa.gov)) ## References ::: {#refs} ::: </br> ***Data Sources:***\ National Coral Reef Monitoring Program Florida Benthic ([NCRMP](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.nodc:NCRMP-Benthic-Florida))\ Florida Fish and Wildlife Conservation Commission's Disturbance Response Monitoring ([DRM](https://coraldrm.org/))